Boanerges Aleman-Meza

Ontology-driven Web services composition platform

Discovering and assembling individual Web services into more complex yet new and more useful Web processes is an important challenge. In this paper, we present techniques for (semi) automatically composing Web services into Web processes by using their ontological descriptions and relationships to other services. In interface-matching automatic composition technique, the possible compositions are obtained by checking semantic similarities between interfaces of individual services. Then these compositions are ranked and an optimum composition is selected. In Human-Assisted composition the user selects a service from a ranked list at certain stages. We also address automatic compositions in a peer-to-peer network.

Ranking complex relationships on the semantic Web

Industry and academia are both focusing their attention on information retrieval over semantic metadata extracted from the Web, and it is increasingly possible to analyze such metadata to discover interesting relationships. However, just as document ranking is a critical component in todays search engines, the ranking of complex relationships would be an important component in tomorrows semantic Web engines. This article presents a flexible ranking approach to identify interesting and relevant relationships in the semantic Web. The authors demonstrate the schemes effectiveness through an empirical evaluation over a real-world data set.

Semantic Association Identification and Knowledge Discovery for National Security Applications

Public and private organizations have access to a vast amount of internal, deep Web and open Web information. Transforming this heterogeneous and distributed information into actionable and insightful information is the key to the emerging new classes of business intelligence and national security applications. Although the role of semantics in search and integration has been often talked about, in this paper we discuss semantic approaches to support analytics on vast amounts of heterogeneous data. In particular, we bring together novel academic research and commercialized Semantic Web technology. The academic research related to semantic association identification is built upon commercial Semantic Web technology for semantic metadata extraction. A prototypical demonstration of this research and technology is presented in the context of an aviation security application of significance to national security.

SwetoDblp ontology of Computer Science publications

SwetoDblp is a large populated ontology with a shallow schema yet a large number of real-world instance data. We describe how such ontology is built from an XML source and how it can be maintained. Instead of a one-to-one mapping from XML to RDF, the creation of the ontology emphasizes the addition of relationships and the value of URIs. SwetoDblp is publicly available online. We also summarize research efforts that have used or are using this freely available community resource.

Combining RDF Vocabularies for Expert Finding

This paper presents a framework for the reuse and extension of existing, established vocabularies in the Semantic Web. Driven by the primary application of expert finding, we will explore the reuse of vocabularies that have attracted a considerable user community already (FOAF, SIOC, etc.) or are derived from de facto standards used in tools or industrial practice (such as vCard, iCal and Dublin Core). This focus guarantees direct applicability and low entry barriers, unlike when devising a new ontology from scratch. The Web is already populated with several vocabularies which complement each other (but also have considerable overlap) in that they cover a wide range of necessary features to adequately describe the expert finding domain. Little effort has been made so far to identify and compare existing approaches, and to devise best practices on how to use and extend various vocabularies conjointly. It is the goal of the recently started ExpertFinder initiative to fill this gap. In this paper we present the ExpertFinder framework for reuse and extension of existing vocabularies in the Semantic Web. We provide a practical analysis of overlaps and options for combined use and extensions of several existing vocabularies, as well as a proposal for applying rules and other enabling technologies to the expert finding task.

Discovering and ranking semantic associations over a Large RDF metabase

Information retrieval over semantic metadata has recently received a great amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this data is becoming an active research topic. Just as ranking of documents is a critical component of todays search engines, the ranking of relationships will be essential in tomorrows semantic analytics engines. Building upon our recent work on specifying these semantic relationships, which we refer to as Semantic Associations, we demonstrate a system where these associations are discovered among a large semantic metabase represented in RDF. Additionally we employ ranking techniques to provide users with the most interesting and relevant results.

Differences in transcription between free-living and CO2-activated third-stage larvae of Haemonchus contortus

BackgroundThe disease caused by Haemonchus contortus, a blood-feeding nematode of small ruminants, is of major economic importance worldwide. The infective third-stage larva (L3) of this gastric nematode is enclosed in a cuticle (sheath) and, once ingested with herbage by the host, undergoes an exsheathment process that marks the transition from the free-living (L3) to the parasitic (xL3) stage. This study explored changes in gene transcription associated with this transition and predicted, based on comparative analysis, functional roles for key transcripts in the metabolic pathways linked to larval development.ResultsTotals of 101,305 (L3) and 105,553 (xL3) expressed sequence tags (ESTs) were determined using 454 sequencing technology, and then assembled and annotated; the most abundant transcripts encoded transthyretin-like, calcium-binding EF-hand, NAD(P)-binding and nucleotide-binding proteins as well as homologues of Ancylostoma-secreted proteins (ASPs). Using an in silico-subtractive analysis, 560 and 685 sequences were shown to be uniquely represented in the L3 and xL3 stages, respectively; the transcripts encoded ribosomal proteins, collagens and elongation factors (in L3), and mainly peptidases and other enzymes of amino acid catabolism (in xL3). Caenorhabditis elegans orthologues of transcripts that were uniquely transcribed in each L3 and xL3 were predicted to interact with a total of 535 other genes, all of which were involved in embryonic development.ConclusionThe present study indicated that some key transcriptional alterations taking place during the transition from the L3 to the xL3 stage of H. contortus involve genes predicted to be linked to the development of neuronal tissue (L3 and xL3), formation of the cuticle (L3) and digestion of host haemoglobin (xL3). Future efforts using next-generation sequencing and bioinformatic technologies should provide the efficiency and depth of coverage required for the determination of the complete transcriptomes of different developmental stages and/or tissues of H. contortus as well as the genome of this important parasitic nematode. Such advances should lead to a significantly improved understanding of the molecular biology of H. contortus and, from an applied perspective, to novel methods of intervention.

Multi-endpoint, High-Throughput Study of Nanomaterial Toxicity in Caenorhabditis elegans

The booming nanotechnology industry has raised public concerns about the environmental health and safety impact of engineered nanomaterials (ENMs). High-throughput assays are needed to obtain toxicity data for the rapidly increasing number of ENMs. Here we present a suite of high-throughput methods to study nanotoxicity in intact animals using Caenorhabditis elegans as a model. At the population level, our system measures food consumption of thousands of animals to evaluate population fitness. At the organism level, our automated system analyzes hundreds of individual animals for body length, locomotion speed, and lifespan. To demonstrate the utility of our system, we applied this technology to test the toxicity of 20 nanomaterials at four concentrations. Only fullerene nanoparticles (nC60), fullerol, TiO2, and CeO2 showed little or no toxicity. Various degrees of toxicity were detected from different forms of carbon nanotubes, graphene, carbon black, Ag, and fumed SiO2 nanoparticles. Aminofullerene and ultraviolet-irradiated nC60 also showed small but significant toxicity. We further investigated the effects of nanomaterial size, shape, surface chemistry, and exposure conditions on toxicity. Our data are publicly available at the open-access nanotoxicity database www.QuantWorm.org/nano.

Scalable semantic analytics on social networks for addressing the problem of conflict of interest detection

In this article, we demonstrate the applicability of semantic techniques for detection of Conflict of Interest (COI). We explain the common challenges involved in building scalable Semantic Web applications, in particular those addressing connecting-the-dots problems. We describe in detail the challenges involved in two important aspects on building Semantic Web applications, namely, data acquisition and entity disambiguation (or reference reconciliation). We extend upon our previous work where we integrated the collaborative network of a subset of DBLP researchers with persons in a Friend-of-a-Friend social network (FOAF). Our method finds the connections between people, measures collaboration strength, and includes heuristics that use friendship/affiliation information to provide an estimate of potential COI in a peer-review scenario. Evaluations are presented by measuring what could have been the COI between accepted papers in various conference tracks and their respective program committee members. The experimental results demonstrate that scalability can be achieved by using a dataset of over 3 million entities (all bibliographic data from DBLP and a large collection of FOAF documents).

Systematic profiling of Caenorhabditis elegans locomotive behaviors reveals additional components in G-protein Gαq signaling

Genetic screens have been widely applied to uncover genetic mechanisms of movement disorders. However, most screens rely on human observations of qualitative differences. Here we demonstrate the application of an automatic imaging system to conduct a quantitative screen for genes regulating the locomotive behavior in Caenorhabditis elegans. Two hundred twenty-seven neuronal signaling genes with viable homozygous mutants were selected for this study. We tracked and recorded each animal for 4 min and analyzed over 4,400 animals of 239 genotypes to obtain a quantitative, 10-parameter behavioral profile for each genotype. We discovered 87 genes whose inactivation causes movement defects, including 50 genes that had never been associated with locomotive defects. Computational analysis of the high-content behavioral profiles predicted 370 genetic interactions among these genes. Network partition revealed several functional modules regulating locomotive behaviors, including sensory genes that detect environmental conditions, genes that function in multiple types of excitable cells, and genes in the signaling pathway of the G protein Gαq, a protein that is essential for animal life and behavior. We developed quantitative epistasis analysis methods to analyze the locomotive profiles and validated the prediction of the γ isoform of phospholipase C as a component in the Gαq pathway. These results provided a system-level understanding of how neuronal signaling genes coordinate locomotive behaviors. This study also demonstrated the power of quantitative approaches in genetic studies.